Centralized vs Decentralized Multi-Agent Cooperative Trajectory Planning via Model Predictive Control

Kurzfassung

Path and trajectory planning for multi-agent systems is computationally heavy when vehicle dynamics are involved. If a centralized setup is used, the computational burden scales exponentially with the number of agents due to dependent decisions and interactions between them. To dampen this effect the classical idea is decentralization. However, optimality gets lost and in a model predictive control setup major replanning effects occur if no consensus is enforced. To overcome these issues, we propose another planning layer providing decisions and additional information to significantly simplify the dynamics based lower-level optimization.